"""Standard LangChain interface tests.""" from typing import Literal import pytest from langchain_core.language_models import BaseChatModel from langchain_core.rate_limiters import InMemoryRateLimiter from langchain_core.tools import BaseTool from langchain_tests.integration_tests import ( ChatModelIntegrationTests, ) from langchain_groq import ChatGroq rate_limiter = InMemoryRateLimiter(requests_per_second=0.2) class TestGroq(ChatModelIntegrationTests): @property def chat_model_class(self) -> type[BaseChatModel]: return ChatGroq @property def chat_model_params(self) -> dict: return {"model": "llama-3.3-70b-versatile", "rate_limiter": rate_limiter} @pytest.mark.xfail(reason="Not yet implemented.") def test_tool_message_histories_list_content( self, model: BaseChatModel, my_adder_tool: BaseTool ) -> None: super().test_tool_message_histories_list_content(model, my_adder_tool) @pytest.mark.xfail( reason="Groq models have inconsistent tool calling performance. See: " "https://github.com/langchain-ai/langchain/discussions/19990" ) def test_bind_runnables_as_tools(self, model: BaseChatModel) -> None: super().test_bind_runnables_as_tools(model) @property def supports_json_mode(self) -> bool: return True @pytest.mark.parametrize("schema_type", ["pydantic", "typeddict", "json_schema"]) def test_json_schema( schema_type: Literal["pydantic", "typeddict", "json_schema"], ) -> None: class JsonSchemaTests(ChatModelIntegrationTests): @property def chat_model_class(self) -> type[ChatGroq]: return ChatGroq @property def chat_model_params(self) -> dict: return {"model": "openai/gpt-oss-120b", "rate_limiter": rate_limiter} @property def structured_output_kwargs(self) -> dict: return {"method": "json_schema"} test_instance = JsonSchemaTests() model = test_instance.chat_model_class(**test_instance.chat_model_params) JsonSchemaTests().test_structured_output(model, schema_type)